| /* |
| * Licensed to the Apache Software Foundation (ASF) under one or more |
| * contributor license agreements. See the NOTICE file distributed with |
| * this work for additional information regarding copyright ownership. |
| * The ASF licenses this file to You under the Apache License, Version 2.0 |
| * (the "License"); you may not use this file except in compliance with |
| * the License. You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, software |
| * distributed under the License is distributed on an "AS IS" BASIS, |
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| * See the License for the specific language governing permissions and |
| * limitations under the License. |
| */ |
| |
| package org.apache.opennlp.ml.maxent; |
| |
| import java.io.IOException; |
| |
| import org.apache.opennlp.ml.model.FileEventStream; |
| import org.apache.opennlp.ml.model.OnePassRealValueDataIndexer; |
| import org.apache.opennlp.ml.model.RealValueFileEventStream; |
| |
| |
| import junit.framework.TestCase; |
| |
| public class RealValueModelTest extends TestCase { |
| |
| public void testRealValuedWeightsVsRepeatWeighting() throws IOException { |
| RealValueFileEventStream rvfes1 = new RealValueFileEventStream("src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt"); |
| GISModel realModel = GIS.trainModel(100,new OnePassRealValueDataIndexer(rvfes1,1)); |
| |
| FileEventStream rvfes2 = new FileEventStream("src/test/resources/data/opennlp/maxent/repeat-weighting-training-data.txt"); |
| GISModel repeatModel = GIS.trainModel(100,new OnePassRealValueDataIndexer(rvfes2,1)); |
| |
| String[] features2Classify = new String[] {"feature2","feature5"}; |
| double[] realResults = realModel.eval(features2Classify); |
| double[] repeatResults = repeatModel.eval(features2Classify); |
| |
| assertEquals(realResults.length, repeatResults.length); |
| for(int i=0; i<realResults.length; i++) { |
| System.out.println(String.format("classifiy with realModel: %1$s = %2$f", realModel.getOutcome(i), realResults[i])); |
| System.out.println(String.format("classifiy with repeatModel: %1$s = %2$f", repeatModel.getOutcome(i), repeatResults[i])); |
| assertEquals(realResults[i], repeatResults[i], 0.01f); |
| } |
| |
| features2Classify = new String[] {"feature1","feature2","feature3","feature4","feature5"}; |
| realResults = realModel.eval(features2Classify, new float[] {5.5f, 6.1f, 9.1f, 4.0f, 1.8f}); |
| repeatResults = repeatModel.eval(features2Classify, new float[] {5.5f, 6.1f, 9.1f, 4.0f, 1.8f}); |
| |
| System.out.println(); |
| assertEquals(realResults.length, repeatResults.length); |
| for(int i=0; i<realResults.length; i++) { |
| System.out.println(String.format("classifiy with realModel: %1$s = %2$f", realModel.getOutcome(i), realResults[i])); |
| System.out.println(String.format("classifiy with repeatModel: %1$s = %2$f", repeatModel.getOutcome(i), repeatResults[i])); |
| assertEquals(realResults[i], repeatResults[i], 0.01f); |
| } |
| |
| } |
| } |